1. Field of the Invention
The present invention relates to techniques for detecting faults in systems. More specifically, the present invention relates to a method and apparatus that approximates a functional relationship from a training data through nonparametric regression, and uses the approximated functional relationship to inferentially estimate a system variable.
2. Related Art
Modern server systems are typically equipped with a significant number of sensors which monitor signals during the operation of the server systems. For example, these monitored signals can include temperatures, voltages, currents, and a variety of software performance metrics, including CPU usage, I/O traffic, and memory utilization. Outputs from this monitoring process can be used to generate time series data for these signals which can subsequently be analyzed to determine how well a computer system is operating.
One particularly useful application of this analysis technique is to facilitate proactive fault detection and monitoring to identify leading indicators of component or system failures before the failures actually occur. Typically, this is achieved by detecting anomalies in the signals which may potentially lead to system failures.
One proactive fault detection and monitoring technique employs functional relationship approximation techniques to predict normal system data values based on other collected information (i.e. “inferential estimation”). Conventional functional relationship approximation techniques can be divided into two categories: parametric techniques and nonparametric techniques.
The parametric techniques make distributional assumptions about underlying data generating mechanisms and detect failures in data sets based on parametric models. However, the results of these parametric techniques suffer from the errors in the distributional assumptions which are used to predict the data generating mechanisms.
On the other hand, the nonparametric techniques do not make distributional assumptions on the data to be modeled. In other words, a nonparametric technique has no (or very little) a priori knowledge about the form of the functional relationship which is being estimated. Although the functional relationship is still modeled using an equation containing free parameters, the nonparametric methods allow the class of functions which the model can represent to be very broad. Furthermore, the nonparametric techniques typically use a large number of free parameters, whereas the parametric techniques typically use only a small number of parameters.
Note that some of the most frequently referenced nonparametric methods include artificial neural networks, kernel regression, support vector machines, and autoassociative memory-based techniques.
Unfortunately, these conventional nonparametric techniques suffer from one or several of the following problems: (1) they require large training data sets to produce accurate predictions, in particular for multidimensional cases; (2) they are computationally intensive because of the iterative nature of learning algorithms; (3) they produce inconsistent estimates of the approximated values due to stochastic optimization of weights; (4) they produce numerically unstable estimates; and (5) they require careful tuning of a bandwidth parameter or careful preprocessing of the training data set.
Hence, what is needed is a nonparametric functional relationship approximation method without the above-described problems.